We use the same train-validation-test split as provided by CMR [1]}. The images in both datasets are annotated with foreground masks and camera poses. Specifically, we pre-train ConvMesh on the pseudo ground truths derived from the training split following a class conditional setting [2]}.
During inference, we conduct GAN inversion on the test split without assuming additional labeled data compared to existing methods. We use the silhouette masks predicted by an off-the-shelf instance segmentation method PointRend [3]} pre-trained on COCO [4]}, which gives an IoU of 0.886 against ground truth masks. We use camera poses predicted by CMR [1]}, which can be inaccurate. In particular, the poses estimated yields 6.03 degree of azimuth error and 4.33 degree of elevation error compared to the ground truth cameras via structure-from-motion (SfM).
During evaluation, we report quantitative results based on ground truth masks.
